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ABSTRACT: Image understanding applications often involve a pattern classification stage. In this paper we show how a fuzzy rule-based classifier, extended to incorporate a cost function, can be successfully used in various imaging applications. The antecedent part of fuzzy if-then rules are specified by partitioning each attributes into fuzzy sets while the consequent class and the degree of certainty are determined from compatibility training patterns. Extension to include a cost term is shown to be straightforward and experimental results on several image processing tasks demonstrate the efficacy of our method.
Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on; 09/2009
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ABSTRACT: Estimating the parameters of biochemical networks from time-courses is becoming increasingly important. There have been some attempts in the past to carry out this task in an automatic way. In this research, SASS, a novel heuristic optimisation algorithm that has only one control parameter, has been used to solve this problem. While the obtained estimations are similar to those using other recent techniques, the method presented here offers a better resistance to local minima and a decrease of a 20% in average in computational cost, without the need of finding a suitable set of control parameters.
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on; 07/2008
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ABSTRACT: Colour quantisation is a common image processing technique where full colour images are to be displayed using a limited palette. The choice of a good palette is therefore crucial as it directly determines the quality of the resulting image. Standard quantisation approaches typically try to minimise the (squared) error between the original and the quantised image which does not correspond well to how humans perceive the images. In this paper we introduce a new colour quantisation algorithm that is designed not to minimise these errors but to maximise the image quality as evaluated by S-CIELAB, an image quality metric that has been shown to work well for various image processing tasks. Experimental results based on a set of standard images demonstrate the superiority in terms of achieved image quality of our novel method
Image Processing, 2006 IEEE International Conference on; 11/2006
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ABSTRACT: Color palettes are inherent to color quantized images and represent the range of possible colors in such images. When converting full true color images to palletized counterparts, the color palette should be chosen so as to minimize the resulting distortion compared to the original. In this paper, we show that in contrast to previous approaches on color quantization, which rely on either heuristics or clustering techniques, a generic optimization algorithm such as a self-adaptive hybrid genetic algorithm can be employed to generate a palette of high quality. Experiments on a set of standard test images using a novel self-adaptive hybrid genetic algorithm show that this approach is capable of outperforming several conventional color quantization algorithms and provide superior image quality.
Systems, Man and Cybernetics, 2006. SMC '06. IEEE International Conference on; 11/2006
